The Optimization Model of in Job-Shop Scheduling Problem with Alternative Machines Based on Improved Genetic Algorithm

2014 ◽  
Vol 607 ◽  
pp. 569-572 ◽  
Author(s):  
Qing Chi ◽  
Xiu Li Fu ◽  
Ya Nan Pan ◽  
Zeng Hui An
Keyword(s):  
Optimization Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Processing Technology ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Manufacturing Enterprise ◽  
Shop Scheduling ◽  
Discrete Manufacturing ◽  
Alternative Machines

The job-shop scheduling problem with alternative machines is very complicated and hard to simplify during product management system for discrete manufacturing enterprise. According to the integrated constraint condition of the processing technology and equipment resources, an optimization model for the dispatch plan of processing technology for the gear shaft assembly is analyzed and established in this paper. Furthermore, the optimization results for the process sequence planning of the gear shaft assembly are obtained by iterative algorithm and improved genetic algorithms approach. The calculating program of optimization layout is developed by Matlab. The optimization results show that the production cycle time and operating cost is reduced remarkably and the efficiency is also improved. Through analysis and verification, it is optimal and feasible for discrete manufacturing enterprise in engineering applications.

Multi-Objective Optimization Algorithm for Job Shop Scheduling Problem in Discrete Manufacturing Enterprise

2015 ◽  
Vol 741 ◽  
pp. 860-864
Author(s):  
Li Lan Liu ◽  
Xue Wei Liu ◽  
Sen Wang ◽  
Wei Zhou ◽  
Gai Ping Zhao
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Manufacturing Enterprise ◽  
Shop Scheduling ◽  
Discrete Manufacturing ◽  
Scheduling Plan

Job Shop scheduling should satisfy the constraints of time, order and resource. To solve this NP-Hard problem, multi-optimization for job shop scheduling problem (JSSP) in discrete manufacturing plant is researched. Objective of JSSP in discrete manufacturing enterprise was analyzed, and production scheduling optimization model was constructed with the optimization goal of minimizing the bottleneck machines’ make-span and the total products’ tardiness; Then, Particle Swarm Optimization (PSO) algorithm was used to solve this model by the process-based encoding mode; To solve the premature convergence problem of PSO, advantages of Simulated Annealing (SA) algorithm, such as better global optimization performance, was integrated into PSO algorithm and a Hybrid PSO-SA Algorithm (HPSA) was proposed and the flowchart was presented; Then, this hybrid algorithm was applied in actual production scheduling of a discrete manufacturing enterprise. Finally, comparative analysis of HPSA/SA/PSO optimal methods and actual scheduling plan was carried out, which verify the result that the HPSA is effective and superiority.

scholarly journals A Sequence List Algorithm For The Job Shop Scheduling Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 55-61
Author(s):  
Shuli Zhang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Manufacturing Enterprises ◽  
Machine Sequence ◽  
Discrete Manufacturing ◽  
Processing Order ◽  
Process Constraints

For the discrete manufacturing enterprises, the job shop scheduling problem is an important class of actual combinatorial optimization problem with resources and sequence constraints. According to the needs of the job shop scheduling problem, a sequence list algorithm for the job shop scheduling problem was designed in this paper. In order to make all jobs being finished as soon as possible, the goal of the sequence list algorithm is minimizing the maximal the finish time of all operations. In the sequence list algorithm, two types of sequence lists were built. They are the job sequence lists and the machine sequence lists. A job sequence list was used to store all operations of a job on the basis of its process constraints. A machine sequence list which is null initially was used to store all operations on a machine in accordance with the actual processing order. The important tasks of the sequence list algorithm are inserting all operations of the job sequence lists into the machine sequence lists and adjusting the processing order of the operations in the machine sequence lists. The sequence list algorithm could always achieve a good job shop schedule which ensures the select performance indicators. The feasibility and efficiency of the algorithm was verified through examples.

Two-Level Robust Optimization for Uncertain Job Shop Scheduling Problem

2014 ◽  
Vol 1039 ◽  
pp. 514-521
Author(s):  
Bing Wang ◽  
Xiao Yan Li ◽  
He Xia Meng
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Processing Times ◽  
Robustness Measure ◽  
Expected Performance

This paper proposes a two-level robust optimization model in the context of job shop scheduling problem. The job shop scheduling problem optimizes the makespan under uncertain processing times, which are described by a set of scenarios. In the first-level optimization, a traditional stochastic optimization model is conducted to obtain the optimal expected performance as a standard performance, on which a concept of bad-scenario set is defined. In the second-level optimization, a robustness measure is given based on bad-scenario set. The objective function for the second robust optimization model is to combine expected performance and robustness measure. Finally, an extensive experiment was conducted to investigate the advantages of the proposed robust optimization model. The computational results show that the two-level model can achieve a better compromise between average performance and robustness than the existing robust optimization models.

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